• Computers and Concrete
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• v.21 no.4
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• pp.463-470
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• 2018

In the present study, soft computing i.e., machine learning techniques and regression models algorithms have earned much importance for the prediction of the various parameters in different fields of science and engineering. This paper depicts that how regression models can be implemented for the prediction of compressive strength of concrete. Three models are taken into consideration for this; they are Gaussian Process for Regression (GPR), Multi Adaptive Regression Spline (MARS) and Minimax Probability Machine Regression (MPMR). Contents of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate and age in days have been taken as inputs and compressive strength as output for GPR, MARS and MPMR models. A comparatively large set of data including 1030 normalized previously published results which were obtained from experiments were utilized. Here, a comparison is made between the results obtained from all the above mentioned models and the model which provides the best fit is established. The experimental results manifest that proposed models are robust for determination of compressive strength of concrete.

• Structural Engineering and Mechanics
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• v.68 no.6
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• pp.691-700
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• 2018

Ground granulated blast furnace slag (GGBS) is a by product obtained from iron and steel industries, useful in the design and development of high quality cement paste/mortar and concrete. This paper investigates the applicability of relevance vector machine (RVM) based regression model to predict the compressive strength of various GGBS based concrete mixes. Compressive strength data for various GGBS based concrete mixes has been obtained by considering the effect of water binder ratio and steel fibres. RVM is a machine learning technique which employs Bayesian inference to obtain parsimonious solutions for regression and classification. The RVM is an extension of support vector machine which couples probabilistic classification and regression. RVM is established based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. Compressive strength model has been developed by using MATLAB software for training and prediction. About 70% of the data has been used for development of RVM model and 30% of the data is used for validation. The predicted compressive strength for GGBS based concrete mixes is found to be in very good agreement with those of the corresponding experimental observations.

• Computers and Concrete
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• v.27 no.1
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• pp.21-28
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• 2021

The Artificial Neural Network (ANN) is a system, which is utilized for solving complicated problems by using nonlinear equations. This study aims to investigate compressive strength, rebound hammer number (RN), and ultrasonic pulse velocity (UPV) of sustainable concrete containing various amounts of fly ash, silica fume, and blast furnace slag (BFS). In this study, the artificial neural network technique connects a nonlinear phenomenon and the intrinsic properties of sustainable concrete, which establishes relationships between them in a model. To this end, a total of 645 data sets were collected for the concrete mixtures from previously published papers at different curing times and test ages at 3, 7, 28, 90, 180 days to propose a model of nine inputs and three outputs. The ANN model's statistical parameter R2 is 0.99 of the training, validation, and test steps, which showed that the proposed model provided good prediction of compressive strength, RN, and UPV of sustainable concrete with the addition of cement.

• Smart Structures and Systems
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• v.30 no.2
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• pp.195-207
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• 2022

Computational drawbacks associated with regular predictive models have motivated engineers to use hybrid techniques in dealing with complex engineering tasks like simulating the compressive strength of concrete (CSC). This study evaluates the efficiency of tree potential metaheuristic schemes, namely shuffled complex evolution (SCE), multi-verse optimizer (MVO), and beetle antennae search (BAS) for optimizing the performance of a multi-layer perceptron (MLP) system. The models are fed by the information of 1030 concrete specimens (where the amount of cement, blast furnace slag (BFS), fly ash (FA1), water, superplasticizer (SP), coarse aggregate (CA), and fine aggregate (FA2) are taken as independent factors). The results of the ensembles are compared to unreinforced MLP to examine improvements resulted from the incorporation of the SCE, MVO, and BAS. It was shown that these algorithms can considerably enhance the training and prediction accuracy of the MLP. Overall, the proposed models are capable of presenting an early, inexpensive, and reliable prediction of the CSC. Due to the higher accuracy of the BAS-based model, a predictive formula is extracted from this algorithm.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.57-58
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• 2022

Concrete mix design is used as essential data for the quality of concrete, analysis of structures, and stable use of sustainable structures. However, since most of the formulation design is established based on the experience of experts, there is a lack of data to base it on. are suffering Accordingly, in this study, the purpose of this study is to build a predictive model to use the concrete mixing factor as basic data for calculation using the DNN technique. As for the data set for DNN model learning, OPC and ternary concrete data were collected according to the presence or absence of admixture, respectively, and the model was separated for OPC and ternary concrete, and training was carried out. In addition, by varying the number of hidden layers of the DNN model, the prediction performance was evaluated according to the model structure. The higher the number of hidden layers in the model, the higher the predictive performance for the prediction of the mixing elements except for the compressive strength factor set as the output value, and the ternary concrete model showed higher performance than the OPC. This is expected because the data set used when training the model also affected the training.

• Journal of the Korea Institute of Building Construction
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• v.4 no.4
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• pp.63-70
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• 2004

The objective of this study is to evaluate the compressive strength of recycled aggregate concrete by the non-destructive testing. Main experimental variables were the replacement level of recycled aggregate and blast-furnace slag, which were divided into two series according to recycled aggregate maximum size. Test results showed that a recycled aggregate had a significant influence on the non-destructive testing results, such as rebound number, Ultrasonic pulse velocity, and frequency. A prediction model of compressive strength considering the replacement level of recycled aggregate was suggested by multi-regression analysis and was compared with test results.

• Explosives and Blasting
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• v.29 no.2
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• pp.81-88
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• 2011

Most of the blast pollution that causes complaints is noise and vibration. Hence, special attentions need to be paid to controlling the underwater noise in designing blasting for those areas. This study estimated underwater sound pressure using distance from blasting and charge per delay and underwater sound pressure level using the underwater sound pressure. To identify the validity of the estimated value, the study demonstrated the results at other areas and compared actual results with estimated results.

• Journal of Advanced Marine Engineering and Technology
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• v.35 no.7
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• pp.946-956
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• 2011

The main objectives of this study are to analyze the leaked gas dispersion and quantify the potential overpressures due to vapor cloud explosions in order to identify the most significant contributors to risk by using Computational Fluid Dynamics (CFX & FLACS) for gas fuelled ships. A series of CFD simulations and analyses have been performed for the various gas release scenarios in a closed module, covering different release rates and ventilating methods. This study is specially focused on the LNG FGS (Fuel Gas Supply) system recently developed for the propulsion of VLCC crude oil carriers by shipyards. Most of work presented is discussed on the gas dispersion from leaks in the FGS room, and shows some blast prediction validation examples.

• Computers and Concrete
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• v.9 no.1
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• pp.21-33
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• 2012

This paper presents the numerical simulation of the rigid 12.6 mm diameter kinetic energy ogive-nosed projectile impact on plain and fiber reinforced concrete (FRC) targets with compressive strengths from 45 to 235 MPa, using a three-dimensional finite element code LS-DYNA. A combined dynamic constitutive model, describing the compressive and tensile damage of concrete, is implemented. A modified Johnson_Holmquist_Cook (MJHC) constitutive relationship and damage model are incorporated to simulate the concrete behavior under compression. A tensile damage model is added to the MJHC model to analyze the dynamic fracture behavior of concrete in tension, due to blast loading. As a consequence, the impact damage in targets made of plain and fiber reinforced concrete with same matrix material under same impact velocities (650 m/s) are obtained. Moreover, the damage distribution of concrete after penetration is procured to compare with the experimental results. Numerical simulations provide a reasonable prediction on concrete damage in both compression and tension.

• Structural Engineering and Mechanics
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• v.52 no.1
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• pp.97-113
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• 2014

In the present work appropriate concrete material models have been proposed to predict drying shrinkage and specific creep of High-performance concrete (HPC) using Artificial Neural Network (ANN). The ANN models are trained, tested and validated using 106 different experimental measured set of data collected from different literatures. The developed models consist of 12 input parameters which include quantities of ingredients namely ordinary Portland cement, fly ash, silica fume, ground granulated blast-furnace slag, water, and other aggregate to cement ratio, volume to surface area ratio, compressive strength at age of loading, relative humidity, age of drying commencement and age of concrete. The Feed-forward backpropagation networks with Levenberg-Marquardt training function are chosen for proposed ANN models and same implemented on MATLAB platform. The results shows that the proposed ANN models are more rational as well as computationally more efficient to predict time-dependent properties of drying shrinkage and specific creep of HPC with high level accuracy.